Fault-tolerant flight control remains a major challenge as aircraft systems become increasingly autonomous and must operate under uncertain conditions and potential actuator failures. Reinforcement learning has shown strong potential for learning control policies directly from interaction with the environment, but purely offline-trained agents often lack the ability to adapt once deployed.

This thesis proposes a hybrid reinforcement learning framework that combines offline deep reinforcement learning with online adaptive control. A novel RUN-DSAC-IDHP controller is developed, integrating an uncertainty-aware offline policy learned with RUN-DSAC with an online Incremental Dual Heuristic Programming (IDHP) adaptation layer. The offline component provides a high-performance baseline policy, while the IDHP actor continuously adapts the control policy online when the aircraft dynamics change.

The proposed approach demonstrates how combining offline deep reinforcement learning with online adaptive control enables controllers to maintain strong baseline performance while adapting in real time to faults and changing flight conditions. ... Read more